Lateral classification of vehicle behavior for automated logic scenario generation
Advanced testing and validation in the automotive industry are predominantly conducted through real-world driving, while simulation is primarily used for scenarios that are difficult, dangerous, or impractical to reproduce on the road. The production of a well-designed simulation scenario includes n...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2535068 |
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| Summary: | Advanced testing and validation in the automotive industry are predominantly conducted through real-world driving, while simulation is primarily used for scenarios that are difficult, dangerous, or impractical to reproduce on the road. The production of a well-designed simulation scenario includes numerous edge cases and relies on expert labor, but it can still miss real-world complexities. Given that, scenarios based on real-world driving data are more desirable, but are dominantly based on replaying the positions of all vehicles over time. For greater flexibility, real-world driving scenarios should be automatically converted into logic scenarios with adjustable segments such as lane keeping, lane changing, accelerating, etc. This paper introduces a new method for detecting and classifying lateral vehicle movements. The method is based on polynomial fitting and identifies the start and end points of vehicle lane changes with inflection points of a third-degree polynomial. For evaluation, two publicly available, manually labeled lane-change datasets are used, and a novel highway lane-change simulation dataset is introduced. The polynomial approach is compared against a bidirectional long short-term memory (Bi-LSTM) model introduced in this work, as well as three additional neural network architectures drawn from recent literature, followed by a discussion of the experimental validation for each. |
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| ISSN: | 0005-1144 1848-3380 |